{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "ec1238e9-8454-49e2-9094-23a9e09f61fc",
      "metadata": {
        "id": "ec1238e9-8454-49e2-9094-23a9e09f61fc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.ticker import MaxNLocator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df9e989d-cb90-452e-9bb6-15e7b77016c3",
      "metadata": {
        "id": "df9e989d-cb90-452e-9bb6-15e7b77016c3",
        "outputId": "7e98819f-1b46-4110-b306-84997f302355"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1639"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Count Number of Unique Files Read\n",
        "import os\n",
        "\n",
        "# Define the folder path\n",
        "folder_path = \"output_al_final\"\n",
        "\n",
        "# Get all files in the folder and its subfolders\n",
        "unique_files = set()\n",
        "\n",
        "for root, _, files in os.walk(folder_path):\n",
        "    for file in files:\n",
        "        unique_files.add(file)\n",
        "\n",
        "# Count the number of unique files\n",
        "num_unique_files = len(unique_files)\n",
        "num_unique_files\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6f4971ab-af90-4d39-b7fb-ed7513db350a",
      "metadata": {
        "id": "6f4971ab-af90-4d39-b7fb-ed7513db350a",
        "outputId": "3a625cce-ccb8-4fce-9af7-2035c75a2e8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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Corp._1675033_20200319.csv', '10-Q_Silver Spike Investment Corp._1843162_20230512.csv', '10-K_Sonnet BioTherapeutics Holdings Inc._1106838_20221215.csv', '10-K_PENNANTPARK INVESTMENT CORP_1383414_20081120.csv', '10-K_Firsthand Technology Value Fund Inc._1495584_20170314.csv', '10-K_BlackRock Capital Investment Corp_1326003_20210303.csv', '10-K_Oxford Square Capital Corp._1259429_20230323.csv', '10-K_Garrison Capital Inc._1509892_20150304.csv', '10-K_TCW Direct Lending LLC_1603480_20230406.csv', '10-K_Flat Rock Capital Corp._1701724_20190314.csv', '10-K_Venture Lending & Leasing IX Inc._1717310_20190314.csv', '10-K_Full Circle Capital Corp_1490013_20100928.csv', '10-K_GLADSTONE CAPITAL CORP_1143513_20211115.csv', '10-K_Aventura Holdings Inc._878802_20080331.csv', '10-K_HMS INCOME FUND INC._1535778_20180321.csv', '10-K_Phillip Street Middle Market Lending Fund LLC_1948368_20230302.csv', '10-K_FS Investment CORP_1422183_20120330.csv', '10-K_Owl Rock Core Income Corp._1812554_20210316.csv', '10-K_Fuse Science Inc._842722_20220223.csv', '10-K_ARES CAPITAL CORP_1287750_20230207.csv', '10-K_CION Investment Corp_1534254_20140320.csv', '10-K_AMERICAN CAPITAL LTD_817473_20090302.csv', '10-K_HERCULES TECHNOLOGY GROWTH CAPITAL INC_1280784_20120309.csv', '10-K_Muzinich BDC Inc._1779523_20210331.csv', '10-K_Investcorp Credit Management BDC Inc._1578348_20210914.csv', '10-Q_Capitala Finance Corp._1571329_20131114.csv', '10-K_Audax Credit BDC Inc._1633858_20220325.csv', '10-K_HMS INCOME FUND INC._1535778_20160311.csv', '10-K_Oaktree Strategic Income II Inc._1744179_20201216.csv', '10-K_AMERICAN CAPITAL LTD_817473_20100301.csv', '10-Q_Stellus Private Credit BDC_1901037_20221104.csv', '10-K_UTEK CORP_1098482_20010322.csv', '10-K_Brightwood Capital Corp I_1895316_20230321.csv', '10-K_CROSSROADS CAPITAL INC._1444706_20170331.csv', '10-K_CAPITAL SOUTHWEST CORP_17313_20220524.csv', '10-K_Parkview Capital Credit Inc._1626899_20180402.csv', '10-K_Corporate Capital Trust II_1617896_20180302.csv', '10-K_APOLLO INVESTMENT CORP_1278752_20070529.csv', '10-K_Corporate Capital Trust Inc._1494538_20150320.csv', '10-Q_TCP Capital Corp._1370755_20121108.csv', '10-K_PennantPark Floating Rate Capital Ltd._1504619_20151112.csv', '10-K_Corporate Capital Trust Inc._1494538_20120316.csv', '10-K_KAYNE ANDERSON ENERGY DEVELOPMENT CO_1363890_20090213.csv', '10-K_Owl Rock Capital Corp II_1655887_20210305.csv', '10-K_Chanticleer Holdings Inc._1106838_20180330.csv', '10-K_Newtek Business Services Corp._1587987_20190318.csv', '10-K_KEATING CAPITAL INC_1444706_20120301.csv', '10-K_Fifth Street Senior Floating Rate Corp._1577791_20161214.csv', '10-Q_CION Investment Corp_1534254_20121114.csv', '10-K_BlackRock Kelso Capital CORP_1326003_20090316.csv', '10-K_OFS Capital Corp_1487918_20200313.csv', '10-K_Crescent Capital BDC Inc._1633336_20220223.csv', '10-K_SCP Private Credit Income BDC LLC_1743415_20210224.csv', '10-K_BDC Capital Inc._1065598_20060524.csv', '10-K_Horizon Technology Finance Corp_1487428_20150310.csv', '10-K_United Health Products Inc._1096938_20160513.csv', '10-K_Venture Lending & Leasing VIII Inc._1642862_20220314.csv', '10-K_Morgan Stanley Direct Lending Fund_1782524_20200320.csv', '10-K_Firsthand Technology Value Fund Inc._1495584_20200316.csv', '10-Q_Nuveen Churchill Direct Lending Corp._1737924_20201106.csv', '10-K_MVC CAPITAL INC._1099941_20190114.csv', '10-K_Fifth Street Senior Floating Rate Corp._1577791_20141210.csv', '10-K_Monroe Capital Income Plus Corp_1742313_20190313.csv', '10-K_BlackRock Capital Investment Corp_1326003_20200304.csv', '10-K_ARES CAPITAL CORP_1287750_20080228-checkpoint.csv', '10-K_Lafayette Square Gulf Coast BDC Inc._1879018_20220325.csv', '10-K_Oaktree Specialty Lending Corp_1414932_20171129.csv', '10-K_WESTERN PENNSYLVANIA ADVENTURE CAPITAL FUND_1023957_20090217.csv', '10-K_CHINA HGS REAL ESTATE INC._1158420_20210113.csv', '10-K_TriplePoint Private Venture Credit Inc._1792509_20220308.csv', '10-Q_GOLUB CAPITAL DIRECT LENDING CORP_1868878_20230511.csv', '10-K_Special Value Continuation Partners LP_1370754_20150310.csv', '10-K_MVC CAPITAL INC._1099941_20140113.csv', '10-Q_CLICKER INC._1107998_20100720.csv', '10-K_GLADSTONE CAPITAL CORP_1143513_20161121.csv', '10-K_POKER MAGIC INC_1425355_20100226.csv', '10-K_TPG Specialty Lending Inc._1508655_20150224.csv', '10-K_Universal Capital Management Inc._1308569_20070731.csv', '10-K_Oaktree Strategic Income Corp_1577791_20201119.csv', '10-K_TECHNEST HOLDINGS INC_1077800_20111013.csv', '10-K_PSI CORP_888702_20120127.csv', '10-K_Medley Capital Corp_1490349_20171207.csv', '10-K_GOLUB CAPITAL BDC Inc._1476765_20181128.csv', '10-K_Kayne Anderson BDC Inc._1747172_20210226.csv', '10-K_GLADSTONE INVESTMENT CORPORATION-DE_1321741_20130514.csv', '10-K_Carlyle Credit Solutions Inc._1702510_20230314.csv'}\n"
          ]
        }
      ],
      "source": [
        "al_files = {file.replace('_AL', '') for file in unique_files}\n",
        "\n",
        "# Printing the result\n",
        "print(al_files)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d74f583a-a35d-483c-9c8c-c533983cf446",
      "metadata": {
        "id": "d74f583a-a35d-483c-9c8c-c533983cf446",
        "outputId": "2ada6236-12b7-43e1-be5d-1955e2f52778"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "6139"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Count Number of Unique Files Read\n",
        "import os\n",
        "\n",
        "# Define the folder path\n",
        "folder_path = \"output_inv_final\"\n",
        "\n",
        "# Get all files in the folder and its subfolders\n",
        "unique_files = set()\n",
        "\n",
        "for root, _, files in os.walk(folder_path):\n",
        "    for file in files:\n",
        "        unique_files.add(file)\n",
        "\n",
        "# Count the number of unique files\n",
        "num_unique_files = len(unique_files)\n",
        "num_unique_files\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "836c8bb8-b1d5-4cb7-b71c-2c83fed3ad1c",
      "metadata": {
        "id": "836c8bb8-b1d5-4cb7-b71c-2c83fed3ad1c"
      },
      "outputs": [],
      "source": [
        "inv_files = {\n",
        "    '_'.join(file.split('_')[:-2]) + '.csv'\n",
        "    for file in unique_files\n",
        "}\n",
        "#print(inv_files)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "142da403-5329-4b52-8629-12d26fb48afd",
      "metadata": {
        "id": "142da403-5329-4b52-8629-12d26fb48afd",
        "outputId": "3807c286-223d-45e2-cf81-45fb337d9f18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'10-K_ACCESS CAPITAL STRATEGIES COMMUNITY INVESTMENT FUND INC-MA_1040241_20050812.csv',\n",
              " '10-K_ADVANTAGE CAPITAL DEVELOPMENT CORP_54175_20050714.csv',\n",
              " '10-K_ADVANTAGE CAPITAL DEVELOPMENT CORP_54175_20060808.csv',\n",
              " '10-K_AG Twin Brook Capital Income Fund_1913724_20230317.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20090302.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20100301.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20110228.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20120227.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20130301.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20140303.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20150302.csv',\n",
              " '10-K_AMERICAN CAPITAL LTD_817473_20160217.csv',\n",
              " '10-K_AMERICAN CAPITAL STRATEGIES LTD_817473_20030331.csv',\n",
              " '10-K_AMERICAN CAPITAL STRATEGIES LTD_817473_20040309.csv',\n",
              " '10-K_AMERICAN CAPITAL STRATEGIES LTD_817473_20050315.csv',\n",
              " '10-K_AMERICAN CAPITAL STRATEGIES LTD_817473_20060314.csv',\n",
              " '10-K_AMERICAN CAPITAL STRATEGIES LTD_817473_20070301.csv',\n",
              " '10-K_AMERICAN CAPITAL STRATEGIES LTD_817473_20080229.csv',\n",
              " '10-K_AMERITRANS CAPITAL CORP_1064015_20080929.csv',\n",
              " '10-K_AMERITRANS CAPITAL CORP_1064015_20090928.csv',\n",
              " '10-K_AMERITRANS CAPITAL CORP_1064015_20100928.csv',\n",
              " '10-K_AMERITRANS CAPITAL CORP_1064015_20110928.csv',\n",
              " '10-K_AMERITRANS CAPITAL CORP_1064015_20120928.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20050614.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20060612.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20070529.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20080529.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20090601.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20100526.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20110531.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20120523.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20130523.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20140520.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20150519.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20160519.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20170518.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20180518.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20190516.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20200521.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20210520.csv',\n",
              " '10-K_APOLLO INVESTMENT CORP_1278752_20220519.csv',\n",
              " '10-K_Alcentra Capital Corp_1578620_20150318.csv',\n",
              " '10-K_Alcentra Capital Corp_1578620_20180314.csv',\n",
              " '10-K_Alcentra Capital Corp_1578620_20190312.csv',\n",
              " '10-K_American Development & Investment Fund Inc._1096935_20100415.csv',\n",
              " '10-K_Apollo Debt Solutions BDC_1837532_20230316.csv',\n",
              " '10-K_BDCA VENTURE INC._1444706_20150326.csv',\n",
              " '10-K_BLACKHAWK CAPITAL GROUP BDC INC_1294345_20090330.csv',\n",
              " '10-K_BLACKHAWK CAPITAL GROUP BDC INC_1294345_20100331.csv',\n",
              " '10-K_BRANTLEY CAPITAL CORP_1021009_20020401.csv',\n",
              " '10-K_BRANTLEY CAPITAL CORP_1021009_20030331.csv',\n",
              " '10-K_BRANTLEY CAPITAL CORP_1021009_20040330.csv',\n",
              " '10-K_BROADLEAF CAPITAL PARTNERS INC_748268_20120403.csv',\n",
              " '10-K_BROADLEAF CAPITAL PARTNERS INC_748268_20130328.csv',\n",
              " '10-K_Bain Capital Specialty Finance Inc._1655050_20170329.csv',\n",
              " '10-K_Bain Capital Specialty Finance Inc._1655050_20190228.csv',\n",
              " '10-K_Bain Capital Specialty Finance Inc._1655050_20210224.csv',\n",
              " '10-K_Bain Capital Specialty Finance Inc._1655050_20220223.csv',\n",
              " '10-K_Barings BDC Inc._1379785_20200227.csv',\n",
              " '10-K_Barings BDC Inc._1379785_20210323.csv',\n",
              " '10-K_Barings BDC Inc._1379785_20220223.csv',\n",
              " '10-K_Barings Capital Investment Corp_1811972_20210323.csv',\n",
              " '10-K_Barings Capital Investment Corp_1811972_20220223.csv',\n",
              " '10-K_Barings Capital Investment Corp_1811972_20230223.csv',\n",
              " '10-K_Barings Private Credit Corp_1859919_20220223.csv',\n",
              " '10-K_Barings Private Credit Corp_1859919_20230223.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20160309.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20170308.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20180307.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20190306.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20200304.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20210303.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20220302.csv',\n",
              " '10-K_BlackRock Capital Investment Corp_1326003_20230301.csv',\n",
              " '10-K_BlackRock Direct Lending Corp._1834543_20210308.csv',\n",
              " '10-K_BlackRock Direct Lending Corp._1834543_20220303.csv',\n",
              " '10-K_BlackRock Direct Lending Corp._1834543_20230302.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20070329.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20080317.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20090316.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20100312.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20110308.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20120301.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20130307.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20140306.csv',\n",
              " '10-K_BlackRock Kelso Capital CORP_1326003_20150305.csv',\n",
              " '10-K_BlackRock Private Credit Fund_1902649_20230302.csv',\n",
              " '10-K_BlackRock TCP Capital Corp._1370755_20190228.csv',\n",
              " '10-K_BlackRock TCP Capital Corp._1370755_20200226.csv',\n",
              " '10-K_BlackRock TCP Capital Corp._1370755_20210225.csv',\n",
              " '10-K_BlackRock TCP Capital Corp._1370755_20220224.csv',\n",
              " '10-K_Blackstone - GSO Secured Lending Fund_1736035_20190318.csv',\n",
              " '10-K_Blackstone - GSO Secured Lending Fund_1736035_20200228.csv',\n",
              " '10-K_Blackstone Private Credit Fund_1803498_20220309.csv',\n",
              " '10-K_Blackstone Secured Lending Fund_1736035_20210304.csv',\n",
              " '10-K_Blackstone Secured Lending Fund_1736035_20220228.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20110331.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20120319.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20130321.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20140319.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20150504.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20160309.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20170316.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20180322.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20190328.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20200320.csv',\n",
              " '10-K_Business Development Corp of America_1490927_20210315.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20090529.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20110610.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20120601.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20140603.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20160614.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20170601.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20180605.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20190604.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20200602.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20210526.csv',\n",
              " '10-K_CAPITAL SOUTHWEST CORP_17313_20220524.csv',\n",
              " '10-K_CION Investment Corp_1534254_20130327.csv',\n",
              " '10-K_CION Investment Corp_1534254_20140320.csv',\n",
              " '10-K_CION Investment Corp_1534254_20150327.csv',\n",
              " '10-K_CION Investment Corp_1534254_20160314.csv',\n",
              " '10-K_CION Investment Corp_1534254_20170316.csv',\n",
              " '10-K_CION Investment Corp_1534254_20180316.csv',\n",
              " '10-K_CION Investment Corp_1534254_20190319.csv',\n",
              " '10-K_CION Investment Corp_1534254_20200317.csv',\n",
              " '10-K_CION Investment Corp_1534254_20210316.csv',\n",
              " '10-K_CION Investment Corp_1534254_20220310.csv',\n",
              " '10-K_CLX INVESTMENT COMPANY INC._317438_20061213.csv',\n",
              " '10-K_CM Finance Inc_1578348_20160830.csv',\n",
              " '10-K_CM Finance Inc_1578348_20180904.csv',\n",
              " '10-K_CROSSROADS CAPITAL INC._1444706_20160329.csv',\n",
              " '10-K_CROSSROADS CAPITAL INC._1444706_20170331.csv',\n",
              " '10-K_Capitala Finance Corp._1571329_20140328.csv',\n",
              " '10-K_Capitala Finance Corp._1571329_20180227.csv',\n",
              " '10-K_Capitala Finance Corp._1571329_20190304.csv',\n",
              " '10-K_Capitala Finance Corp._1571329_20200302.csv',\n",
              " '10-K_Capitala Finance Corp._1571329_20210308.csv',\n",
              " '10-K_Carey Credit Income Fund - I_1618696_20160328.csv',\n",
              " '10-K_Carey Credit Income Fund - I_1618696_20170322.csv',\n",
              " '10-K_Carey Credit Income Fund 2016 T_1618694_20160316.csv',\n",
              " '10-K_Carey Credit Income Fund 2016 T_1618694_20170417.csv',\n",
              " '10-K_Carey Credit Income Fund 2018 T_1674786_20170324.csv',\n",
              " '10-K_Carey Credit Income Fund_1618697_20160316.csv',\n",
              " '10-K_Carey Credit Income Fund_1618697_20170321.csv',\n",
              " '10-K_Carlyle Secured Lending III_1851277_20220304.csv',\n",
              " '10-K_Carlyle Secured Lending III_1851277_20230315.csv',\n",
              " '10-K_Commonwealth Credit Partners BDC I Inc._1841514_20220325.csv',\n",
              " '10-K_Commonwealth Credit Partners BDC I Inc._1841514_20230310.csv',\n",
              " '10-K_CorEnergy Infrastructure Trust Inc._1347652_20130213.csv',\n",
              " '10-K_CorEnergy Infrastructure Trust Inc._1347652_20140318.csv',\n",
              " '10-K_Corporate Capital Trust II_1617896_20170317.csv',\n",
              " '10-K_Corporate Capital Trust II_1617896_20180302.csv',\n",
              " '10-K_Corporate Capital Trust Inc._1494538_20120316.csv',\n",
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              " '10-K_Universal Capital Management Inc._1308569_20140813.csv',\n",
              " '10-K_VENTURE LENDING & LEASING IV INC_1271808_20050325.csv',\n",
              " '10-K_VENTURE LENDING & LEASING IV INC_1271808_20060228.csv',\n",
              " '10-K_VENTURE LENDING & LEASING IV INC_1271808_20070309.csv',\n",
              " '10-K_VENTURE LENDING & LEASING IV INC_1271808_20080331.csv',\n",
              " '10-K_VENTURE LENDING & LEASING IV INC_1271808_20090330.csv',\n",
              " '10-K_VENTURE LENDING & LEASING IV INC_1271808_20100325.csv',\n",
              " '10-K_VENTURE LENDING & LEASING IV INC_1271808_20110325.csv',\n",
              " '10-K_Varagon Capital Corp_1784700_20230327.csv',\n",
              " '10-K_Venture Lending & Leasing V Inc._1376067_20080331.csv',\n",
              " '10-K_Venture Lending & Leasing V Inc._1376067_20090330.csv',\n",
              " '10-K_Venture Lending & Leasing V Inc._1376067_20100325.csv',\n",
              " '10-K_Venture Lending & Leasing V Inc._1376067_20110325.csv',\n",
              " '10-K_Venture Lending & Leasing V Inc._1376067_20120315.csv',\n",
              " '10-K_Venture Lending & Leasing V Inc._1376067_20130315.csv',\n",
              " '10-K_Venture Lending & Leasing V Inc._1376067_20140321.csv',\n",
              " '10-K_Venture Lending & Leasing VI Inc._1482199_20110325.csv',\n",
              " '10-K_Venture Lending & Leasing VI Inc._1482199_20120315.csv',\n",
              " '10-K_Venture Lending & Leasing VI Inc._1482199_20130315.csv',\n",
              " '10-K_Venture Lending & Leasing VI Inc._1482199_20140321.csv',\n",
              " '10-K_Venture Lending & Leasing VI Inc._1482199_20150317.csv',\n",
              " '10-K_Venture Lending & Leasing VI Inc._1482199_20160315.csv',\n",
              " '10-K_Venture Lending & Leasing VI Inc._1482199_20170315.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20130315.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20140321.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20150317.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20160315.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20170315.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20180316.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20190314.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20200316.csv',\n",
              " '10-K_Venture Lending & Leasing VII Inc._1557424_20210315.csv',\n",
              " '10-K_Venture Lending & Leasing VIII Inc._1642862_20160315.csv',\n",
              " '10-K_Venture Lending & Leasing VIII Inc._1642862_20170315.csv',\n",
              " '10-K_Venture Lending & Leasing VIII Inc._1642862_20180316.csv',\n",
              " '10-K_Venture Lending & Leasing VIII Inc._1642862_20190314.csv',\n",
              " '10-K_Venture Lending & Leasing VIII Inc._1642862_20200316.csv',\n",
              " '10-K_Venture Lending & Leasing VIII Inc._1642862_20210315.csv',\n",
              " '10-K_Venture Lending & Leasing VIII Inc._1642862_20230315.csv',\n",
              " '10-K_WTI Fund X Inc._1850938_20230315.csv',\n",
              " '10-K_WhiteHorse Finance Inc._1552198_20130305.csv',\n",
              " '10-K_WhiteHorse Finance Inc._1552198_20190314.csv',\n",
              " '10-K_WhiteHorse Finance Inc._1552198_20200316.csv',\n",
              " '10-K_WhiteHorse Finance Inc._1552198_20210308.csv',\n",
              " '10-K_WhiteHorse Finance Inc._1552198_20220304.csv',\n",
              " '10-Q_ACCESS CAPITAL STRATEGIES COMMUNITY INVESTMENT FUND INC-MA_1040241_20060407.csv',\n",
              " '10-Q_AFC BDC Inc._1892091_20220812.csv',\n",
              " '10-Q_AMERICAN ENTERPRISE DEVELOPMENT CORP_1136725_20051118.csv',\n",
              " '10-Q_AMERITRANS CAPITAL CORP_1064015_20130515.csv',\n",
              " '10-Q_ARES STRATEGIC INCOME FUND_1918712_20230515.csv',\n",
              " '10-Q_Alcentra Capital Corp_1578620_20141110.csv',\n",
              " '10-Q_Barings Capital Investment Corp_1811972_20201109.csv',\n",
              " '10-Q_Barings Private Credit Corp_1859919_20211109.csv',\n",
              " '10-Q_BlackRock Private Credit Fund_1902649_20221107.csv',\n",
              " '10-Q_CITY CAPITAL CORP_793986_20061114.csv',\n",
              " '10-Q_CLX INVESTMENT COMPANY INC._317438_20070515.csv',\n",
              " '10-Q_Capitala Finance Corp._1571329_20131114.csv',\n",
              " '10-Q_Carey Credit Income Fund - I_1618696_20151116.csv',\n",
              " '10-Q_Carey Credit Income Fund 2016 T_1618694_20151116.csv',\n",
              " '10-Q_Carey Credit Income Fund_1618697_20151116.csv',\n",
              " '10-Q_Carlyle Secured Lending III_1851277_20211112.csv',\n",
              " '10-Q_Commonwealth Credit Partners BDC I Inc._1841514_20211112.csv',\n",
              " '10-Q_Corporate Capital Trust Inc._1494538_20111110.csv',\n",
              " '10-Q_Credit Suisse Park View BDC Inc._1619239_20151113.csv',\n",
              " '10-Q_Crescent Capital BDC Inc._1633336_20151116.csv',\n",
              " '10-Q_ENDOVASC INC_1040415_20051121.csv',\n",
              " '10-Q_Firsthand Technology Value Fund Inc._1495584_20111109.csv',\n",
              " '10-Q_GLADSTONE CAPITAL CORP_1143513_20230502.csv',\n",
              " '10-Q_GLADSTONE INVESTMENT CORPORATION-DE_1321741_20051109.csv',\n",
              " '10-Q_GSV Capital Corp._1509470_20111110.csv',\n",
              " '10-Q_GTREX CAPITAL INC._944020_20051114.csv',\n",
              " '10-Q_GUGGENHEIM CREDIT INCOME FUND 2021_1674786_20201116.csv',\n",
              " '10-Q_Golub Capital BDC 4 Inc._1901612_20230512.csv',\n",
              " '10-Q_Golub Capital Direct Lending Unlevered Corp_1901606_20230515.csv',\n",
              " '10-Q_HERCULES TECHNOLOGY GROWTH CAPITAL INC_1280784_20051109.csv',\n",
              " '10-Q_HMS INCOME FUND INC._1535778_20121114.csv',\n",
              " '10-Q_HPS Corporate Lending Fund_1838126_20221114.csv',\n",
              " '10-Q_INNOCAP INC_1281845_20050920.csv',\n",
              " '10-Q_Investcorp Credit Management BDC Inc._1578348_20230515.csv',\n",
              " '10-Q_Investcorp US Institutional Private Credit Fund_1948565_20230515.csv',\n",
              " '10-Q_Kennedy Lewis Capital Co_1911321_20230515.csv',\n",
              " '10-Q_MSD Investment Corp._1849894_20221114.csv',\n",
              " '10-Q_MVC CAPITAL INC._1099941_20110907.csv',\n",
              " '10-Q_MVC CAPITAL INC._1099941_20130909.csv',\n",
              " '10-Q_MacKenzie Realty Capital Inc._1550913_20230515.csv',\n",
              " '10-Q_Main Street Capital CORP_1396440_20071116.csv',\n",
              " '10-Q_Muzinich BDC Inc._1779523_20191114.csv',\n",
              " '10-Q_NF Investment Corp._1561528_20131108.csv',\n",
              " '10-Q_New Mountain Finance AIV Holdings Corp_1515173_20111114.csv',\n",
              " '10-Q_New Mountain Finance Corp_1496099_20111114.csv',\n",
              " '10-Q_New Mountain Finance Holdings L.L.C._1491925_20111114.csv',\n",
              " '10-Q_New Mountain Guardian III BDC L.L.C._1781870_20191113.csv',\n",
              " '10-Q_North Haven Private Income Fund LLC_1851322_20221109.csv',\n",
              " '10-Q_OFS Capital Corp_1487918_20121213.csv',\n",
              " '10-Q_OXFORD FINANCE CORP_1200934_20031114.csv',\n",
              " '10-Q_Oaktree Strategic Credit Fund_1872371_20230511.csv',\n",
              " '10-Q_Owl Rock Capital Corp III_1807427_20201112.csv',\n",
              " '10-Q_Owl Rock Capital Corp_1655888_20161109.csv',\n",
              " '10-Q_PIMCO Capital Solutions BDC Corp._1905824_20221114.csv',\n",
              " '10-Q_Palmer Square Capital BDC Inc._1794776_20201113.csv',\n",
              " '10-Q_Parkview Capital Credit Inc._1626899_20151116.csv',\n",
              " '10-Q_Prospect Floating Rate & Alternative Income Fund Inc._1521945_20230510.csv',\n",
              " '10-Q_Redwood Enhanced Income Corp._1870267_20221114.csv',\n",
              " '10-Q_S3 INVESTMENT COMPANY INC._1161647_20060522.csv',\n",
              " '10-Q_SLR Private Credit BDC II LLC_1932591_20230510.csv',\n",
              " '10-Q_Sierra Income Corp_1523526_20121113.csv',\n",
              " '10-Q_Solar Senior Capital Ltd._1508171_20111101.csv',\n",
              " '10-Q_Special Value Continuation Partners LP_1370754_20121115.csv',\n",
              " '10-Q_Star Mountain Lower Middle-Market Capital Corp_1786835_20211115.csv',\n",
              " '10-Q_Stellus Private Credit BDC_1901037_20221104.csv',\n",
              " '10-Q_TCP Capital Corp._1370755_20121108.csv',\n",
              " '10-Q_TCW Direct Lending LLC_1603480_20141107.csv',\n",
              " '10-Q_TPG Specialty Lending Inc._1508655_20111114.csv',\n",
              " '10-Q_Terra Income Fund 6 Inc._1577134_20191113.csv',\n",
              " '10-Q_Trinity Capital Inc._1786108_20201112.csv',\n",
              " '10-Q_TriplePoint Private Venture Credit Inc._1792509_20201113.csv',\n",
              " '10-Q_VENTURE LENDING & LEASING IV INC_1271808_20041115.csv',\n",
              " '10-Q_Varagon Capital Corp_1784700_20221114.csv',\n",
              " '10-Q_Venture Lending & Leasing V Inc._1376067_20071113.csv',\n",
              " '10-Q_Venture Lending & Leasing VI Inc._1482199_20101112.csv',\n",
              " '10-Q_Venture Lending & Leasing VIII Inc._1642862_20151113.csv',\n",
              " '10-Q_Wellings Real Estate Income Fund_1922947_20221114.csv',\n",
              " '10-Q_Wellings Real Estate Income Fund_1922947_20230214.csv'}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inv_files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32aa2897-7f7f-4d2b-aa2e-4e1912f1a9c5",
      "metadata": {
        "id": "32aa2897-7f7f-4d2b-aa2e-4e1912f1a9c5",
        "outputId": "1cfa71f1-5f1a-4211-d964-fa7eefb33b67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Length of AL files: 1639\n",
            "Length of INV files: 835\n",
            "Total length of unique files across both lists: 1680\n"
          ]
        }
      ],
      "source": [
        "unique_al_files_length = len(al_files)\n",
        "unique_inv_files_length = len(inv_files)\n",
        "\n",
        "# Print results\n",
        "print(\"Length of AL files:\", unique_al_files_length)\n",
        "print(\"Length of INV files:\", unique_inv_files_length)\n",
        "combined_unique_files = al_files.union(inv_files)\n",
        "# Calculate the total length of unique files\n",
        "total_unique_files_length = len(combined_unique_files)\n",
        "\n",
        "# Print result\n",
        "print(\"Total length of unique files across both lists:\", total_unique_files_length)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b985bdd8-538a-43ad-99f6-fcf206ab9876",
      "metadata": {
        "id": "b985bdd8-538a-43ad-99f6-fcf206ab9876",
        "outputId": "34d98f65-c01c-41d4-b6fb-7f9d41b2ce3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Count of files with prefix 10-Q: 209\n",
            "Count of files with prefix 10-K: 1471\n"
          ]
        }
      ],
      "source": [
        "# Count files with prefix 10_Q and 10-K in a set\n",
        "count_10_Q = sum(1 for file in combined_unique_files if file.startswith('10-Q'))\n",
        "count_10_K = sum(1 for file in combined_unique_files if file.startswith('10-K'))\n",
        "\n",
        "print(f\"Count of files with prefix 10-Q: {count_10_Q}\")\n",
        "print(f\"Count of files with prefix 10-K: {count_10_K}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6d44784-7f39-4c94-a540-225a85dcf4d7",
      "metadata": {
        "tags": [],
        "id": "a6d44784-7f39-4c94-a540-225a85dcf4d7"
      },
      "source": [
        "# Figure 5: Assets & Liabilities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61940847-01f6-41e9-a878-3c6c95ec9f5a",
      "metadata": {
        "id": "61940847-01f6-41e9-a878-3c6c95ec9f5a"
      },
      "outputs": [],
      "source": [
        "df_al = pd.read_csv(\"assets_and_liabilities.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ccd5469-68c1-48a4-8d0a-45bc2d29cd24",
      "metadata": {
        "id": "6ccd5469-68c1-48a4-8d0a-45bc2d29cd24"
      },
      "outputs": [],
      "source": [
        "# This is the number of companies in the sample\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1478b0c6-da0e-4b21-b4a7-c3ea3086382e",
      "metadata": {
        "id": "1478b0c6-da0e-4b21-b4a7-c3ea3086382e"
      },
      "outputs": [],
      "source": [
        "# Number of issuers in the sample\n",
        "df_inv = pd.read_csv(\"schedule_of_investments.csv\")\n",
        "combine = set(df_al[\"Company_Name\"].unique()) | set(df_inv[\"company_name\"].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ea362575-ecd9-4651-8ced-637bab338581",
      "metadata": {
        "id": "ea362575-ecd9-4651-8ced-637bab338581",
        "outputId": "96670c9b-699b-4a4d-9474-8ffb4d84176e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "335\n"
          ]
        }
      ],
      "source": [
        "print(len(combine))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30fbc3c5-353d-4b64-ae0a-82a7c6469d95",
      "metadata": {
        "id": "30fbc3c5-353d-4b64-ae0a-82a7c6469d95"
      },
      "source": [
        "## Total Assets per Year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73f7c319-2dcd-4f60-8215-9a82175f6dcf",
      "metadata": {
        "id": "73f7c319-2dcd-4f60-8215-9a82175f6dcf",
        "outputId": "7d743e31-e0c3-45b3-a569-ac95cebeb2e5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "yearly_assets = df_al.groupby('Year')['Total_Assets'].sum().reset_index()\n",
        "yearly_assets['Total_Assets_B'] = yearly_assets['Total_Assets'].astype(float) / 1_000_000_000\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "ax1.bar(yearly_assets['Year'], yearly_assets['Total_Assets_B'], color='black')\n",
        "ax1.set_xlabel('Year')\n",
        "ax1.set_ylabel('Total Assets (in Billions of Dollars)', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "ax1.set_xlim(yearly_assets['Year'].min() - 1, yearly_assets['Year'].max() + 1)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b0cd8cec-61a3-4c4b-833a-ebc9b3cccc2f",
      "metadata": {
        "id": "b0cd8cec-61a3-4c4b-833a-ebc9b3cccc2f"
      },
      "source": [
        "## Figure 6: Total Liabilities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e14f16c-665a-41fc-9973-495cb6aff0be",
      "metadata": {
        "id": "1e14f16c-665a-41fc-9973-495cb6aff0be",
        "outputId": "e411dab2-4150-4479-89ce-68403eb7d5df"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\JAREDE~1\\AppData\\Local\\Temp/ipykernel_39436/2489228840.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['Assets_to_Liabilities'] = df_filtered['Total_Assets'] / df_filtered['Total_Liabilities']\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Filter data starting from 2010\n",
        "df_filtered = df_al[df_al['Year'] >= 2010]\n",
        "\n",
        "# Calculate the ratio of assets to liabilities\n",
        "df_filtered['Assets_to_Liabilities'] = df_filtered['Total_Assets'] / df_filtered['Total_Liabilities']\n",
        "\n",
        "# Group by year and calculate the median ratio\n",
        "median_ratios = df_filtered.groupby('Year')['Assets_to_Liabilities'].median().reset_index()\n",
        "\n",
        "# Convert median ratios to a new column for plotting\n",
        "median_ratios['Median_Assets_to_Liabilities'] = median_ratios['Assets_to_Liabilities']\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Create a bar chart for the median ratio of assets to liabilities\n",
        "ax1.bar(median_ratios['Year'], median_ratios['Median_Assets_to_Liabilities'], color='black')\n",
        "ax1.set_xlabel('Year')\n",
        "ax1.set_ylabel('Median Assets to Liabilities Ratio', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Set x-axis limits\n",
        "ax1.set_xlim(median_ratios['Year'].min() - 1, median_ratios['Year'].max() + 1)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93a261a5-d69b-4e78-ac23-ff87b999df72",
      "metadata": {
        "tags": [],
        "id": "93a261a5-d69b-4e78-ac23-ff87b999df72"
      },
      "source": [
        "## Figure 7: Assets/Liabilities Ratio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1cfcdbf1-832b-4366-b8dc-352610b325e6",
      "metadata": {
        "id": "1cfcdbf1-832b-4366-b8dc-352610b325e6",
        "outputId": "c428b716-ce6e-48c8-bcd7-19010dfaf9e1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\JAREDE~1\\AppData\\Local\\Temp/ipykernel_39436/2320949343.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['Assets_to_Liabilities'] = df_filtered['Total_Assets'] / df_filtered['Total_Liabilities']\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Filter data starting from 2010\n",
        "df_filtered = df_al[df_al['Year'] >= 2010]\n",
        "\n",
        "# Calculate the ratio of assets to liabilities\n",
        "df_filtered['Assets_to_Liabilities'] = df_filtered['Total_Assets'] / df_filtered['Total_Liabilities']\n",
        "\n",
        "# Group by year and calculate the median ratio\n",
        "median_ratios = df_filtered.groupby('Year')['Assets_to_Liabilities'].median()\n",
        "\n",
        "# Plot the median ratio over time\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(median_ratios.index, median_ratios.values, marker='o')\n",
        "plt.title('Median Ratio of Assets to Liabilities (2010 onwards)', fontsize=14)\n",
        "plt.xlabel('Year', fontsize=12)\n",
        "plt.ylabel('Median Assets to Liabilities Ratio', fontsize=12)\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bd6e332c-89d6-470c-b5e2-81ac13958c0c",
      "metadata": {
        "id": "bd6e332c-89d6-470c-b5e2-81ac13958c0c"
      },
      "source": [
        "# Schedule of Investments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "d705e0e1-8177-43a7-8377-0f99fa056cef",
      "metadata": {
        "id": "d705e0e1-8177-43a7-8377-0f99fa056cef"
      },
      "outputs": [],
      "source": [
        "df_inv = pd.read_csv(\"schedule_of_investments.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57432f9b-33fe-444a-9943-7d2c136a6b4d",
      "metadata": {
        "id": "57432f9b-33fe-444a-9943-7d2c136a6b4d"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "5178117b-ef52-4e6f-95e8-f341db0c0260",
      "metadata": {
        "tags": [],
        "id": "5178117b-ef52-4e6f-95e8-f341db0c0260"
      },
      "source": [
        "## Figure 6: Total Number of Loans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "a18efe50-eeb0-4feb-a8b0-7a296504f1d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "a18efe50-eeb0-4feb-a8b0-7a296504f1d7",
        "outputId": "b86be429-d522-4800-a2e3-a302b4a9b14b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Count occurrences of each year\n",
        "num_years = df_inv['year'].value_counts().reset_index()\n",
        "num_years.columns = ['year', 'Count']\n",
        "num_years = num_years.sort_values(by='year')\n",
        "num_years[\"Count_K\"] = num_years[\"Count\"] / 1_000\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(num_years['year'], num_years['Count_K'], color='black')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Loans (in Thousands)')\n",
        "# plt.title('Total Number of Loans Over Time')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "\n",
        "plt.xlim(num_years['year'].min() - 1, num_years['year'].max() + .75)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "9b3a5bb9-c24c-4a66-a6d6-4e067591e43a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9b3a5bb9-c24c-4a66-a6d6-4e067591e43a",
        "outputId": "9347b2b8-f2a2-4d1e-833e-3a5200158b86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Loans in 2014: 4434\n",
            "Number of Loans in 2022: 11350\n"
          ]
        }
      ],
      "source": [
        "# Extracting values for 2014 and 2022\n",
        "value_2014 = num_years[num_years['year'] == 2014]['Count'].values[0] if 2014 in num_years['year'].values else None\n",
        "value_2022 = num_years[num_years['year'] == 2022]['Count'].values[0] if 2022 in num_years['year'].values else None\n",
        "\n",
        "# Printing the values\n",
        "print(f\"Number of Loans in 2014: {value_2014}\")\n",
        "print(f\"Number of Loans in 2022: {value_2022}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc64387f-81a3-4ae8-8326-9537283060a1",
      "metadata": {
        "id": "cc64387f-81a3-4ae8-8326-9537283060a1"
      },
      "source": [
        "## Figure 8: Median Loan Size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "dc72391d-d546-4fe4-b9b5-b72f8254fdb6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "dc72391d-d546-4fe4-b9b5-b72f8254fdb6",
        "outputId": "e31e141a-9747-4bd2-d53b-c58e112309b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-4540b5a63251>:20: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
            "  box = plt.boxplot(data, labels=years, showfliers=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loan Size for 2022:\n",
            "Median: 3.25 million dollars\n",
            "25th Percentile: 0.69 million dollars\n",
            "75th Percentile: 10.72 million dollars\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sorting the DataFrame by 'year' if it's not already sorted\n",
        "df_inv_box = df_inv.sort_values(by='year').copy()\n",
        "\n",
        "df_inv_box['parM'] = df_inv_box['par'] / 1_000_000\n",
        "\n",
        "df_inv_box = df_inv_box[['year', 'parM']].dropna()\n",
        "\n",
        "# Grouping the data by year and extracting par values\n",
        "data = [group['parM'].values for name, group in df_inv_box.groupby('year')]\n",
        "\n",
        "# Extracting unique years for x-axis labels\n",
        "years = df_inv_box['year'].unique()\n",
        "\n",
        "# Creating the box plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "# Creating the box plot with default styling\n",
        "box = plt.boxplot(data, labels=years, showfliers=False)\n",
        "\n",
        "# Setting the median line to black\n",
        "plt.setp(box['medians'], color='black')\n",
        "\n",
        "# Adding titles and labels\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Par Value (in Millions of Dollars)')\n",
        "plt.ylim(None, None)\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Showing the plot\n",
        "plt.show()\n",
        "\n",
        "# Extracting data for 2022\n",
        "if 2022 in df_inv_box['year'].values:\n",
        "    data_2022 = df_inv_box[df_inv_box['year'] == 2022]['parM']\n",
        "    median_2022 = data_2022.median()\n",
        "    percentile_25_2022 = data_2022.quantile(0.25)\n",
        "    percentile_75_2022 = data_2022.quantile(0.75)\n",
        "\n",
        "    print(f\"Loan Size for 2022:\")\n",
        "    print(f\"Median: {median_2022:.2f} million dollars\")\n",
        "    print(f\"25th Percentile: {percentile_25_2022:.2f} million dollars\")\n",
        "    print(f\"75th Percentile: {percentile_75_2022:.2f} million dollars\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b245d5ac-9608-4f07-addf-4a641deb1159",
      "metadata": {
        "tags": [],
        "id": "b245d5ac-9608-4f07-addf-4a641deb1159"
      },
      "source": [
        "## Figure 9 - Distressed Funds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "325cf50a-9c9e-47fb-93e4-9abc0e84ea7c",
      "metadata": {
        "id": "325cf50a-9c9e-47fb-93e4-9abc0e84ea7c",
        "outputId": "5646dcc3-2ca0-40c3-df5d-0eed87df5ad0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Step 1: Create 'gap' columns for different thresholds\n",
        "df_loans_size = df_inv[['year', 'fair', 'par']].dropna()\n",
        "df_loans_size['gap_90'] = df_loans_size[\"fair\"] > 0.9 * df_loans_size[\"par\"]\n",
        "df_loans_size['gap_80'] = df_loans_size[\"fair\"] > 0.8 * df_loans_size[\"par\"]\n",
        "df_loans_size['gap_70'] = df_loans_size[\"fair\"] > 0.70 * df_loans_size[\"par\"]\n",
        "\n",
        "# Step 2: Group by year and calculate the percentage of loans for each threshold\n",
        "df_grouped_90 = df_loans_size.groupby('year')['gap_90'].mean() * 100\n",
        "df_grouped_80 = df_loans_size.groupby('year')['gap_80'].mean() * 100\n",
        "df_grouped_70 = df_loans_size.groupby('year')['gap_70'].mean() * 100\n",
        "\n",
        "# Step 3: Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Using different line styles and markers for distinction\n",
        "plt.plot(df_grouped_70.index, df_grouped_70, label='Fair Value > 70% of Par', linestyle='-', marker='o', color='black')\n",
        "plt.plot(df_grouped_80.index, df_grouped_80, label='Fair Value > 80% of Par', linestyle='--', marker='s', color='black')\n",
        "plt.plot(df_grouped_90.index, df_grouped_90, label='Fair Value > 90% of Par', linestyle=':', marker='^', color='black')\n",
        "\n",
        "plt.ylim(None, 100)\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Percentage (%)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend()\n",
        "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7429bad-e16f-47c9-a28d-ab5fc96e35d0",
      "metadata": {
        "id": "e7429bad-e16f-47c9-a28d-ab5fc96e35d0"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}